MSDS 597 Final Project - Exploratory Data Analysis on IMDb Movie Dataset

1 Motiavtion & Goal

Although movies are mainly made for entertainment, not earning enough profit puts filmmakers in an awkward situation that makes it difficult to keep producing high-quality movies. One most popular quality metric is the score from Internet Movie Database (IMDb). Based on the metadata from IMDb, it would be interesting to analyze what makes a movie more successful than others, both commercially and critically. Therefore, the main goal of this project is to explore the IMDb dataset with focus on profit and IMDb score and present the findings in an intuitive and interactive way.

These are the R packages required for this project.

library(tidyverse)
library(knitr)
library(plotly)
library(ggrepel)
library(DT)
library(tm)
library(openxlsx)

2 Data Sources & Preparation

The dataset used in this project came from the IMDb 5000 Movie Dataset from Kaggle. It recorded information on more than 5000 movies across 66 countries from 1916 to 2016. The dataset is available in a csv format file and is of size 1MB. Note that the original dataset is replaced on Kaggle website, and therefore we cannot access the original one. The following link is where we accessed data: https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset (Yueming Zhang 2017).

The data preparation part consists of the following tasks:

1. Import the data.
2. Clean and tidy up the data to ensure its quality and make it easier to manipulate.
3. Detail data description.

2.1 Load Data

First, we imported the data and checked the dimension and names of all attributes of the data. As shown below, there are 5043 movies recorded, and each record has 28 attributes, including information such as “Movie’s Title”, “Director’s Name”, “Budget of the movie”, and “IMDb score of the movie”.

## [1] 5043   28

##  [1] "color"                     "director_name"            
##  [3] "num_critic_for_reviews"    "duration"                 
##  [5] "director_facebook_likes"   "actor_3_facebook_likes"   
##  [7] "actor_2_name"              "actor_1_facebook_likes"   
##  [9] "gross"                     "genres"                   
## [11] "actor_1_name"              "movie_title"              
## [13] "num_voted_users"           "cast_total_facebook_likes"
## [15] "actor_3_name"              "facenumber_in_poster"     
## [17] "plot_keywords"             "movie_imdb_link"          
## [19] "num_user_for_reviews"      "language"                 
## [21] "country"                   "content_rating"           
## [23] "budget"                    "title_year"               
## [25] "actor_2_facebook_likes"    "imdb_score"               
## [27] "aspect_ratio"              "movie_facebook_likes"

Here is the preview of the raw data.

2.2 Data Tidying & Cleansing

Then, to get the data ready for analysis, some tidying and cleansing works need to be done. First, unnecessary characters in the movie_title, genre and plot_keyword columns were removed.

Second, duplicates in movie_title column were removed as they may affect later analysis. In total, 126 duplicate movies were removed.

## [1] 126

Third, columns that contain currency, such as the budget and gross, may cause problems in later analysis because a few countries were not converted to US dollars, such as “South Korea” and “Japan”. Furthermore, given that all of them are converted to US dollars, we still need to consider the inflation, which makes the problem even more complicated. Thus, only movies from USA were kept for the profitability analysis.

## # A tibble: 4,917 x 4
##    movie_title                 budget country        gross
##    <chr>                        <dbl> <chr>          <int>
##  1 Lady Vengeance          4200000000 South Korea   211667
##  2 Fateless                2500000000 Hungary       195888
##  3 Princess Mononoke       2400000000 Japan        2298191
##  4 Steamboy                2127519898 Japan         410388
##  5 Akira                   1100000000 Japan         439162
##  6 Godzilla 2000           1000000000 Japan       10037390
##  7 Kabhi Alvida Naa Kehna   700000000 India        3275443
##  8 Tango                    700000000 Spain        1687311
##  9 Kites                    600000000 India        1602466
## 10 Red Cliff                553632000 China         626809
## # ... with 4,907 more rows

Then, an new column profitable was added to indicate if a movie is profitable, ‘1’ means profitable (that is, profit \(>\) budget). As this involving both gross and budget columns, only movies from USA have non-empty value for this column. There are 3711 USA films, each of which has 31 attributes. Note that this version of data was saved to an csv file for people who want to focus only on USA films.

## [1] 3711   31

Last, regarding records that contain missing values, to keep the entire dataset as complete as possible, we decided to not remove any rows with missing data and to handle this issue for each individual analysis. For example, when doing genre-wise analysis, those without values for genre variables are excluded from the analysis.

After the cleaning, there are 4917 records remain and each have 30 attributes, and these are saved to a csv file for future use.

## [1] 4917   30

##  [1] "color"                     "director_name"            
##  [3] "num_critic_for_reviews"    "duration"                 
##  [5] "director_facebook_likes"   "actor_3_facebook_likes"   
##  [7] "actor_2_name"              "actor_1_facebook_likes"   
##  [9] "gross"                     "genres"                   
## [11] "actor_1_name"              "movie_title"              
## [13] "num_voted_users"           "cast_total_facebook_likes"
## [15] "actor_3_name"              "facenumber_in_poster"     
## [17] "plot_keywords"             "movie_imdb_link"          
## [19] "num_user_for_reviews"      "language"                 
## [21] "country"                   "content_rating"           
## [23] "budget"                    "title_year"               
## [25] "actor_2_facebook_likes"    "imdb_score"               
## [27] "aspect_ratio"              "movie_facebook_likes"     
## [29] "genres_new"                "plot_keywords_new"

Additionally, 3700 out of 4917 records do not have any missing value. They are also saved to a csv file.

## [1] 3700

2.3 Data Description

The following table gives the name, type, and description of each variable in the dataset.

Name	Type	Description
color	character	Colorization: Color or Black and White
director_name	character	Name of the director
num_critic_for_reviews	integer	Number of Critical Reviews
duration	integer	Duration of the movie in Minutes
director_facebook_likes	integer	Number of FB Page Likes of Director
actor_3_facebook_likes	integer	Number of FB Page Likes of Actor No.3
actor_2_name	character	Name of Actor No.2
actor_1_facebook_likes	integer	Number of FB Page Likes of Actor No.1
gross	integer	Gross Earned in US Dollars
genres	character	Classification: Action, Comedy, Drama, …, etc.
actor_1_name	character	Name of Actor No.1
movie_title	character	Title of the Movie
num_voted_users	integer	Number of Voted Users on IMDB
cast_total_facebook_likes	integer	Total FB Page Likes of of the Entire Cast
actor_3_name	character	Name of Actor No.3
facenumber_in_poster	integer	Number of the Actors Featured in the Movie Poster
plot_keywords	character	Keywords Describing the Plot
movie_imdb_link	character	IMDB Link of the Movie
num_user_for_reviews	integer	Number of Users who Reviewed the Movie
language	character	Language of the movie: English, French, Chinese, …, etc.
country	character	Country where the Movie was Produced
content_rating	character	Content rating
budget	double	Budget in US Dollars
title_year	integer	Year of Release
actor_2_facebook_likes	integer	Number of FB Page Likes of Actor No.2
imdb_score	double	IMDB Score on a Scale of 1 to 10
aspect_ratio	double	Aspect Ratio
movie_facebook_likes	integer	Number of FB Page Likes of the Film
genres_new	character	Edited genres
plot_keywords_new	character	Edited plot_keywords

3 Exploratory Data Analysis

The analysis is focused on four main aspects: genre, country, IMDb score, and how they relate to profitability.

3.1 Genre-wise Analysis

In the first part of the analysis, the focus is on the film genres as well as their relationship with profitability. Since most of movies in this dataset were categorized as multiple genres, some preprocessing on the genres must be done before actually analyzing the data.

3.1.1 Genres Preprocessing

First, a document-term matrix for genres was constructed using the package “TM”.

## <<DocumentTermMatrix (documents: 4917, terms: 26)>>
## Non-/sparse entries: 14127/113715
## Sparsity           : 89%
## Maximal term length: 11
## Weighting          : term frequency (tf)

Then, the created document-term matrix was used to calculate frequency for each genre.

## # A tibble: 26 x 2
##    genre     count
##    <chr>     <dbl>
##  1 drama      2533
##  2 comedy     1847
##  3 thriller   1364
##  4 action     1113
##  5 romance    1084
##  6 adventure   888
##  7 crime       868
##  8 sci-fi      594
##  9 fantasy     583
## 10 horror      539
## # ... with 16 more rows

3.1.2 Genres Distribution

After preprocessing was done, we first try to find which genres are used the most. By plotting distribution of genres frequency, we can see that the top 5 movie genres are “Drama”, “Comedy”, “Thriller”, “Action”, and “Romance”.

3.1.3 Genres vs. Profitability vs. IMDb Score

Now, we want to identify which genre tend to have higher profitability as well as ratings. As mentioned before, this part involves movies only from USA due to the currency conversion issue.

First, we calculate the average budget, gross, and profit (= gross - budget) for each genre.

## # A tibble: 23 x 5
##    genres_new  mean_gross mean_budget mean_profit mean_imdb
##    <chr>            <dbl>       <dbl>       <dbl>     <dbl>
##  1 Action       85881165.   70503681.   15377484.      6.24
##  2 Adventure   107588235.   80902013.   26686222.      6.41
##  3 Animation   120847003.   85607853.   35239151.      6.64
##  4 Biography    46089398.   29135662.   16953735.      7.10
##  5 Comedy       54540529.   35059454.   19481075.      6.14
##  6 Crime        44945166.   33948142.   10997025.      6.50
##  7 Documentary  14683626.    4021501.   10662126.      6.84
##  8 Drama        42460107.   29585071.   12875036.      6.72
##  9 Family       94310623.   64866065.   29444559.      6.19
## 10 Fantasy      90539880.   66073493.   24466387.      6.23
## # ... with 13 more rows

Then, we compared them using a grouped bar chart sorted by a descending order of the mean profit. And here are what I’ve found:

1. Top 3 most profitable genres are “Animation”, “Family”, and “Musical”.
2. In general, films of genres of bigger budget (\(> \$80M\)) tend to earn more profit, with “Action” being the only exception.
3. Low-budget films of genres such as “Musical” and “Comedy” are really good investments.
4. All the top 5 popular genres (“Drama”, “Comedy”, “Thriller”, “Action”, and “Romance”) have average profit less than \(\$20M\).
5. Most profitable genres are not highly rated.

3.2 Country-related Analysis

The second part of the analysis aims to explore the nationality component of the data. But, we first plotted the nubmer of movies released per year in the histogram below. From the plot, we see very few movies produced until the 80s and the movie production started to increase significantly around the mid 90s. Note that “Year 2009” has the most movies (253) released in a year by far.

Then, we showed the number of movies produced in each country through the years in the following heat map. We can see that most of the countries started their movie production in the early 2000s. Combined with previous graph, here are some insights that we’ve found:

1. US has the most productive film industry. It has been producing significantly since the late 70s.
2. The only other countries that had a certain level of movie production before 70s are: UK, France, Germany, Japan, and Italy.

Next, we tried to find which language is used most frequently. Unsurprisingly, English is the language in which most movies were made, and US produces movies in most languages (14).

Nonetheless, as can be seen from above result, this dataset consists predominantly of films from the US, we believe it may not be a suitable dataset for us to analyze the profitability and rating of a film from a nationality perspective.

3.3 IMDb Score Analysis

Last, we tried to analyze a film’s successfulness based on its IMDb score.

First, in order to understand the distribution of scores of all films, we plot a histogram of IMDb score altogether with several summary statistics. The mean IMDb Score is 6.4, the median is 6.6, the lowest score is 1.6, and the highest score is 9.3. Furthermore, by computing the 10th and 90th percentile, we can see that 80% of movies have a score between 7.7 and 5.1. For a movie to be a top \(10\%\) in the worlds, it has to have a score of at least 7.7.

##    imdb_score   
##  Min.   :1.600  
##  1st Qu.:5.900  
##  Median :6.600  
##  Mean   :6.464  
##  3rd Qu.:7.200  
##  Max.   :9.300

## 10% 90% 
## 5.1 7.7

Here is the IMDb score distribution.

3.3.1 Interesting Facts

• Top 10 movies with highest IMDb score

• Top 10 directors with highest average IMDb score

3.3.2 IMDb Score vs. Profitabilty

To understand the relationship between IMDB score, gross, and budget, we plotted a 3D scatter plot using the package “plotly” to try to gain a big picture about it. This part, again, involves films from the USA. Note that a green point means it is profitable while a blue one means it’s not and that a bigger point means greater profit earned. Since this is an interactive plot, we can rotate the graph to exmaine the relationship between any two of the three variables.

From the plot, we can see that movies with higher IMDb score tend to have higher profit and significant number of movies ended up losing money. This aligns with my intuition, IMDb score and gross might be correlated as people are more willing to watch famous and highly-rated movies. We also can observe that bigger budget does not guarantee the quality.

3.3.2.1 Commercial Success v.s. Critical Acclaim

However, people may be more interested in those films with huge commerical success, top \(1\%\) (\(\sim 40\)) most profitable (highest profit) films were plotted with their gross and IMDb scores. Note that the vertical and the horizontal line refers to the median gross and the median IMDb score, and bigger point means higher profit earned. Taking a closer look at relationship of these films with their IMDb ratings, we see little correlation between them. This is as expected since generally highly-rated films don’t do very well on box office. However, we can observe that all but two of these 40 films have a score at least 6.4.

Then, these top \(1\%\) movies were plotted again, but, with their profits and IMDb scores, where bigger point means higher IMDb score. For movies with budget over 70 millions dollars, we can observe an upward trend close to linear, which can be inferred that bigger-budget movies tend to earn more profit. However, there’s a slightly downward trend when the budget is less than 70 millions dollars. However, we found that these movies were mostly produced in the 80s or early 90s, such as “E.T. the Extra-Terrestrial” and “Star Wars: Episode IV - A New Hope”, so their true budget should be higher with ticket-inflation being taken into consideration. Therefore, we believe that, if being inflation adjusted, we will observe a more strictly increasing trend in this graph.

3.3.2.2 Return on Investment as Profitability

However, the profit earned does not give a whole picture about financial success of a movie throughout the years, so “Return on Investment (ROI)” is used to provide a different perspective about a movie’s profitability. The following graph shows top \(1\%\) highest Return on Investment movies of at least 10 millions dollars budget. As expected, films with smaller budget have higher ROI and the ROI decreases as the budget grows bigger. Yet, we can see that the ROIs for movies with over \(\$20M\) budget do not differ much. Also, from this graph and the previous one, we can see that “Star Wars: Episode IV - A New Hope” and “E.T. the Extra-Terrestrial” are really two outstandingly successful films, in terms of IMDb score, Profit, and Return on Investment.

References

Yueming Zhang. 2017. IMDB 5000 Movie Dataset | Kaggle. https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset.